Orientation-Aware Model Predictive Control with Footstep Adaptation for Dynamic Humanoid Walking

TL;DR

This paper introduces an orientation-aware MPC framework for humanoid robots that plans footstep locations online using an augmented rigid body model, improving robustness and terrain adaptability.

Contribution

It presents a novel MPC approach using aSRBM that incorporates orientation dynamics and footstep planning within a unified optimization framework.

Findings

Enhanced robustness against external torque disturbances.

Effective traversal of uneven terrains like wave fields.

Real-time implementation via quadratic programming.

Abstract

This paper proposes a novel orientation-aware model predictive control (MPC) for dynamic humanoid walking that can plan footstep locations online. Instead of a point-mass model, this work uses the augmented single rigid body model (aSRBM) to enable the MPC to leverage orientation dynamics and stepping strategy within a unified optimization framework. With the footstep location as part of the decision variables in the aSRBM, the MPC can reason about stepping within the kinematic constraints. A task-space controller (TSC) tracks the body pose and swing leg references output from the MPC, while exploiting the full-order dynamics of the humanoid. The proposed control framework is suitable for real-time applications since both MPC and TSC are formulated as quadratic programs. Simulation investigations show that the orientation-aware MPC-based framework is more robust against external torque disturbance compared to state-of-the-art controllers using the point mass model, especially when the torso undergoes large angular excursion. The same control framework can also enable the MIT Humanoid to overcome uneven terrains, such as traversing a wave field.